Results 111 to 120 of about 86,248 (260)

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving. [PDF]

Sensors (Basel), 2023
Mahima KTY, Perera A, Anavatti S, Garratt M.   +3 more
europepmc   +1 more source

Adversarially Robust Decision Transformer

Advances in Neural Information Processing Systems 37
Accepted to NeurIPS ...
Tang, Xiaohang, Marques, Afonso, Kamalaruban, Parameswaran, Bogunovic, Ilija   +3 more
openaire   +2 more sources

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation

Entropy
Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training.
Yutaro Yamada, Fred Weiying Zhang, Yuval Kluger, Ilker Yildirim   +3 more
doaj   +1 more source

Performance–Robustness Tradeoffs in Adversarially Robust Control and Estimation

IEEE Transactions on Automatic Control
arXiv admin note: substantial text overlap with arXiv:2203 ...
Bruce D. Lee, Thomas T.C.K. Zhang, Hamed Hassani, Nikolai Matni   +3 more
openaire   +2 more sources

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Adversarial Robustness of Self-Supervised Learning Features

IEEE Open Journal of Signal Processing
As deep learning models have proliferated, concerns about their reliability and security have also increased. One significant challenge is understanding adversarial perturbations, which can alter a model's predictions despite being very small in ...
Nicholas Mehlman, Shri Narayanan
doaj   +1 more source

Holistic Adversarially Robust Pruning

Accepted by ICLR ...
Zhao, Qi, Wressnegger, Christian
openaire   +2 more sources

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks.
Wei Xue, Yonghao Wang, Shaoquan Zhang, Xiao Zheng, Ping Zhong   +4 more
doaj   +1 more source